README.md

---
license: apache-2.0
language:
- en
- zh
base_model: prithivMLmods/Viper-Coder-HybridMini-v1.3
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- trl
- coder
- 7B
- llama-cpp
- gguf-my-repo
---

# Triangle104/Viper-Coder-HybridMini-v1.3-Q4_K_S-GGUF
This model was converted to GGUF format from [`prithivMLmods/Viper-Coder-HybridMini-v1.3`](https://huggingface.co/prithivMLmods/Viper-Coder-HybridMini-v1.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/prithivMLmods/Viper-Coder-HybridMini-v1.3) for more details on the model.

---
Viper-Coder-HybridMini-v1.3
-


Viper-Coder-HybridMini-v1.3 is based on the Qwen 2.5 7B modality architecture, designed to be the best
 for coding and reasoning tasks. It has been fine-tuned on a synthetic 
dataset leveraging the latest coding logits and CoT datasets, further 
optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex coding tasks, instruction-following, and text generation.  



	
		
	

Key Improvements
-


Best-in-Class Coding Proficiency: Enhanced understanding of programming languages, debugging, and code generation.  
Fine-Tuned Instruction Following: Optimized for precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (8K+ tokens).  
Advanced Logical & Mathematical Reasoning: Improved multi-step problem-solving and theorem proving.  
Long-Context Mastery: Handles up to 128K tokens with an output capability of 8K tokens per response.  
Multilingual Code Support: Excels in Python, JavaScript, C++, Java, SQL, and other major programming languages, with documentation in 29+ languages.



	
		
	

Quickstart with Transformers
-


from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Viper-Coder-HybridMini-v1.3"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Write a Python function to merge two sorted lists."
messages = [
    {"role": "system", "content": "You are an advanced AI assistant with expert-level coding and reasoning abilities."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)




	
		
	

Intended Use
-


Elite Coding & Debugging: Best-in-class model for writing, analyzing, and optimizing code.  
Complex Algorithmic Reasoning: Solves intricate logic problems and algorithm-based challenges.  
Scientific & Mathematical Computation: Advanced support for formulas, equations, and theorem verification.  
Structured Data Processing: Seamlessly handles JSON, XML, SQL, and data pipeline automation.  
Multilingual Programming Support: Proficient in Python, JavaScript, C++, Java, Go, and more.  
Extended Technical Content Generation: Ideal for writing documentation, research papers, and technical blogs.



	
		
	

Limitations
-


Moderate Computational Demand: Requires GPUs/TPUs for smooth inference due to 7B parameters, but more lightweight than larger models.  
Language-Specific Variability: Performance may vary across different programming languages.  
Possible Error Propagation: Extended text outputs might introduce logical inconsistencies.  
Limited Real-World Awareness: The model does not have access to real-time internet updates.  
Prompt Sensitivity: Performance depends on how well the prompt is structured.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Viper-Coder-HybridMini-v1.3-Q4_K_S-GGUF --hf-file viper-coder-hybridmini-v1.3-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Viper-Coder-HybridMini-v1.3-Q4_K_S-GGUF --hf-file viper-coder-hybridmini-v1.3-q4_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Viper-Coder-HybridMini-v1.3-Q4_K_S-GGUF --hf-file viper-coder-hybridmini-v1.3-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Viper-Coder-HybridMini-v1.3-Q4_K_S-GGUF --hf-file viper-coder-hybridmini-v1.3-q4_k_s.gguf -c 2048
```